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Wednesday, 4 November 2026: 4 new papers, 1 cross-list and 6 replacements in General Economics (econ.GN), in arXiv's order, with full abstracts. The same papers as arXiv's own listing, set as a newspaper; every title links to arXiv.
Modern program evaluation prioritizes evidence from randomized controlled trials (RCTs) because prior research found that observational estimates of program impacts often diverge substantially from experimental benchmarks. We revisit this finding --- which was based on small survey datasets --- in the era of large-scale administrative data. Using anonymized income tax records covering all American adults between 1999 and 2025, we estimate the effects of workforce training programs on earnings by using machine learning models to predict counterfactuals from rich data on prior work histories, demographics, and residential locations. Rather than testing whether our observational estimates are unbiased, we use 37 RCTs to estimate the distribution of their bias. We find that our machine learning estimates closely match the RCT estimates, with root mean squared error (RMSE) comparable to that of the RCTs themselves. Our method reduces RMSE by 60% relative to parametric estimators used in prior work because our larger samples and richer covariates permit more flexible, higher-dimensional models of the counterfactual. Applying our method to a broader set of sites and enrollment years beyond the experimental sample, we find that treatment effects on earnings vary substantially across locations and over time, even within a given program. Programs that place more participants into target-sector jobs generate the largest earnings gains, with impacts that persist even after participants leave their initial jobs. These findings provide guidance for improving job training programs and, more broadly, illustrate a scalable approach to program evaluation that uses observational data to construct experimentally validated ``digital twins.''
Frontier AI labs compete in a contest for commercial leadership while sharing the risk of catastrophe. Each lab gains from arriving earlier but bears only part of the hazard its speed adds, which this paper calls the hazard externality. In an illustrative calibrated takeoff model, three racing labs get through the takeoff period in 18 months, while a common rule on research shares would stretch it to 69 months. Neither force alone explains racing that fast. Removing the contest first closes over ninety percent of the gap in catastrophe probability between racing and a common rule when the catastrophe probability at full speed is 75 percent, and 69 percent when it is 25 percent; averaged over both orders of removal, the contest explains 65 and 44 percent. More labs make each lab race harder, when industry hazard at a common research share does not depend on the number of labs. Under open source in its ideal form, where every lab publishes its capability findings at once, labs slow to the coordinated pace, because no lab can get ahead and each bears all the hazard its research adds. One lab's restraint achieves much less than a common rule. The White House Accord on Super Intelligence of September 2026 audits safety but sets no limit on speed. In the model it does not slow the race, because each lab still gains as much from speeding up.
Rival powers have agreed to limit nuclear arms, phase out ozone-depleting chemicals, and cut greenhouse gases, though each agreement was hard to reach. This paper asks what an agreement to pause frontier AI needs. A follower advances through own research and diffusion from the leader. Because diffusion fades as the gap closes, a paused leader stays ahead longer than its raw lead suggests. A solved agreement game adds voluntary entry, costly monitoring, secret advances, and credible responses. Cooperation is supportable when the monitoring needed to deter a breakout costs no more than either country gains from cooperating. As compute grows during a pause, a secret advance reaches the frozen frontier sooner, shrinking the response window: agreements have a clock. In an example with a fixed leader window, cooperation is supportable through month 18.9. Verified matched safety research extends this to month 25.5, and halving the monitoring cost to month 31.8.
Demand often drops sharply at whole-dollar prices. To explain this phenomenon, known as left-digit bias, we develop a random utility model in which consumers consider a good only if its price is below a heterogeneous attention threshold. Masses of consumers with integer attention thresholds generate demand discontinuities. When a tax is introduced, the model predicts a weaker demand response compared to rational choice for left-digit biased consumers. Using data from a tax experiment, we find that the model matches behavioral moments and partially identify the distribution of willingness to pay relative to thresholds. A weak demand response need not imply a low excess burden of taxation and understanding the specific mechanism is important for welfare analysis.
Using quarterly FRED and CSO Ireland data over 1995Q1-2025Q4, we examine whether U.S.\ IT investment and Irish data-centre electricity are recomposing the Kitchin cycle around energy- and compute-backed assets. Three findings stand out. First, a Quandt-Andrews sup-Wald test locates a significant break in the U.S.\ IT-GDP correlation at 2014Q3 (sup-W $= 9.69$; $r$ falls from $+0.45$ to $-0.12$); the Fisher $r$-to-$z$ test survives Bonferroni adjustment ($z = +3.11$, $p_{\text{Bonf}} = 0.006$), whereas the analogous test on the imposed Cloud-vs-AI boundary does not ($p_{\text{Bonf}} = 0.164$), so ``Strategic Decoupling'' pre-dates the AI Era by approximately six years. Second, in Ireland the synchronous AI-Era correlation between metered data-centre electricity and Real GDP is large ($r = +0.41$, $N = 16$) but \emph{collapses to $r \approx 0$ when Real GDP is replaced by Real GNP}, suggesting the headline is largely an artefact of the shared multinational tax-structure footprint between hyperscaler operations and Irish gross output. Third, the Irish cross-correlation profile is asymmetric in the opposite direction to a leading-indicator reading: the largest positive coefficients cluster on the $k < 0$ side (GDP precedes data-centre load), so compute capacity is better read as coincident or moderately lagging. Given the small AI-Era samples and the absence of a quarterly multinational-adjusted aggregate such as GNI*, all coefficients are exploratory; subject to these caveats, the U.S.\ Kitchin cycle is best dated to 2014Q3, and the Irish Energy-Data Nexus is dominated by multinational firm activity.